FTC Bureau of Economics

Roundtable on the Economics of Internet Auctions:

An Executive Summary

Christopher P. Adams♦

June 28, 2006

Abstract

On October 27, 2005, the Bureau of Economics at the Federal Trade Commission hosted a non-public roundtable on the Economics of Internet Auctions. This one-day roundtable broughttogether academic experts, industry professionals and government economists to discuss and learnabout Internet auctions. This paper is a brief summary of the papers presented and panel discussionsat the roundtable. The roundtable covered three main issues: (1) fraud and information problems onconsumer-to-consumer Internet auction sites like eBay, (2) competition between auction sites andamongst auction site users, and (3) the data generated from auction sites and what inferences may bedrawn from such data. The keynote address was given by Prof. Hal Varian. Varian discussed theauctions used by Google to sell “keyword” searches.

Thanks to all the participants and all those who helped organize this roundtable, including Bernadette Harmon,Peter Newberry, and James Weikamp, as well as BE management and staff. Thanks to Denis Breen, PaulPautler and James Weikamp for their comments on this paper. The views expressed are those of the authorand do not necessarily reflect the views either of the Federal Trade Commission or any individualCommissioner. All errors are my own.Internet Auction Roundtable Christopher P. Adams Page 2

Introduction

In October 2005 the FTC’s Bureau of Economics hosted a roundtable on the economics ofInternet auctions. 1 This roundtable brought together academics, industry professionals andgovernment economists to discuss a number of important issues related to Internet auctions.The roundtable had three main themes; fraud and information problems, competition andcompetition policy, and inference from Internet auction data. These three themescorrespond to concerns deriving from the FTC’s consumer protection, antitrust and researchroles. Internet auctions have become an important way to sell and exchange everythingfrom Beanie Babies to cars and keyword searches. 2 E-commerce’s largest companies,including eBay, Amazon, Yahoo!, and Google, derive a substantial proportion of theirrevenue from Internet auctions. Internet auctions are increasingly used to sell expensiveitems such as cars and houses. 3 Google and Yahoo! both use Internet auctions to sellkeyword searches and these companies are expanding their use of these mechanisms to sellother advertising including television spots. 4 Fraud in Internet auctions, however, is consistently the second largest consumercomplaint (behind identity theft) received by the FTC. Further, among fraud complaints,Internet auctions account for the largest proportion in the FTC complaint database,Consumer Sentinel. 5 However, according to eBay only 1% of transactions result incomplaints from users (Dellarocas and Wood (2005)). EBay and other firms use feedbackmechanisms to help reduce fraud. Do these mechanisms work? How safe are these tradingplatforms? Could fraud be reduced? Internet auctions also raise a number of competition issues including competitionamong users of a particular site and competition between sites. The New York Attorney

1 For more information on the roundtable including a transcript, speaker biographies and related researchpapers please go to http://www.ftc.gov/be/workshops/internetauction/internetauction.htm.2 Google and Yahoo! use auction mechanisms to sell the right to advertise on webpages that show the resultsfrom particular searches. These are called “keyword searches” because the advertising is based on thekeywords used by the searcher. For example, someone searching for information on Disneyland resorts mayview advertising from companies that have bought the right to advertise on any page that shows the results for“Disneyland” or for “resorts” (Varian (2006)).3 See Adams et al. (2006) for a discussion of new and used Corvette sales on eBay.4 Kenneth Li, “Time Warner Mulls TV Ad Auctioning System,” Reuters (4/10/06) athttp://today.reuters.com/business/newsArticle.aspx?type=media&storyID=nN10286316.5 For more information go to http://www.consumer.gov/sentinel/.

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General, Eliot Spitzer, has prosecuted a number of cases in which eBay users have beenaccused of shill bidding, an antitrust violation in New York State. 6 EBay is one of the mostpopular Internet sites and the most popular Internet auction site. Does eBay compete withother Internet sites such as Google, Amazon and Yahoo!? Are there network externalities inInternet auction sites and what are the implications for pricing and competition? As Internet auctions become more popular as a means of selling and exchanginggoods and services, they also become more useful for estimating demand for those goodsand services. Agencies such as the Federal Trade Commission may be interested in usingsuch information to estimate the impact of mergers or the cost of fraud. Agencies such asthe Bureau of Economic Analysis or the Bureau of Labor Statistics may be interested inusing such information for estimating price changes and productivity effects. According toNobel Laureate William Vickrey’s seminal work, eBay’s auction mechanism suggests thatbidders will bid their true value for the item (Vickrey (1961)). Given this result, it may bepossible to estimate demand from simply observing bids for a particular item. Of course,things are never so simple. Do Internet auction prices and bids provide useful informationabout how much things are worth?

Fraud and Information Problems

The roundtable included presentations on fraud and information problems in Internet

auctions from a number of academics, including Pai-Ling Yin (Harvard Business School),Ginger Jin (University of Maryland), Ali Hortacsu (University of Chicago) and Luis Cabral(NYU Stern School of Business). The academic presentations were followed by twopresentations by staff from the FTC, one on how the FTC works to combat fraud on theInternet and the other on the reliability of data from the FTC’s Consumer Sentinel database. Pat Bajari (University of Michigan) provided an introduction and overview ofInternet auctions. 7 Bajari highlighted two important results from the academic research.First, as shown by Pai-Ling Yin, auction prices fall in direct proportion to the amount of

6 Shilling is said to occur when a seller secretly bids in her own auction. She may do this in order to increasethe price she receives for an item and convince unsuspecting bidders that her item is legitimate and highlyvaluable.7 Much of this presentation was based on Bajari and Hortacsu (2004).

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information dispersion in the auction. That is, the less information provided by the sellerthe lower the price the seller receives. Second, sellers lie, at least according to Ginger Jin,about the quality of the baseball cards that they are selling. It is not clear how eBay’sfeedback system helps solve information and fraud problems, and how such problemsimpact trade on the site. In their seminal work on the theory of auctions, Milgrom and Weber (1982) showthat sellers have an incentive to commit to provide buyers with the most completeinformation about the value of the item that they are selling. Is there a trade-off betweeninformation and price? Pat Bajari and Pai-Ling Yin presented recent work of Yin’s whichshows that there is such a trade off (Yin (2005)). Yin collected statements by sellers aboutthe product – a used computer, and then surveyed friends and friends of friends to ask themhow much they would value the item (how much would they suggest a friend pay for theitem) given the information provided by the seller. Yin argues that if there is considerabledispersion in the answer to this question, the seller is probably not providing very accurateinformation. Further she shows that as the amount of dispersion increases, the actual pricepaid in the auction falls. Yin also finds that information and seller reputation arecomplementary. In particular, if a seller with a good reputation does not provide goodinformation, the price is much lower than if a seller with a poor reputation fails to providegood information. Is it worse than being a little vague? Is there fraud on eBay? Ginger Jin presentedsome results from her analysis of baseball cards (Jin and Kato (2002)). Jin and Kato boughtungraded baseball cards, collected information on what the seller stated the card was worth,and then sent the card to be independently graded. Jin and Kato consistently found thatsellers overstate the actual grade of the card. In particular, sellers that state the card is of avery high quality are almost always lying. Jin and Kato found that sellers that claim highgrades are much more likely to fail to deliver the card or to deliver a fake card. The FederalTrade Commission collects data on consumer complaints, and fraud in Internet auctions isthe Consumer Sentinel’s second largest complaint behind identity theft. However, thatstatistic may be misleading. This database is not a random sample of the population.Companies such as eBay actively encourage consumers to register their complaints on thedatabase. The FTC’s Keith Anderson showed that in a random sample of such complaints,most complainants first learned of the firm or the product on the Internet (57%) (Anderson

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(2005)). Comparing this to data from a random survey of consumers, Anderson found thatonly 14% first learn about the product from the Internet. What are the consequences of fraud and information problems? The existence offraud makes buyers more cautious and lowers the prices received by legitimate sellers. Thisis the classic Akerlof-lemons problem (Akerlof (1970)). As buyers can’t tell which sellers arelegitimate and which are hucksters, the price received by all sellers falls. That is, bidderslower their bid to account for the probability that the auction is fraudulent. Fraud andinformation problems may also lead to an inefficient allocation of products. For examplethere may be a same-city bias for buyers. 8 Ali Hortacsu discussed some preliminary researchon the geography of trade on eBay. Hortacsu does find evidence of a same-city bias, evenafter accounting for measured transportation costs. Hortacsu points out that the existenceof information problems on the site is only one explanation for same-city bias and othersinclude similarity in tastes (Hortacsu et al (2005)). What is being done about fraud and information problems on the Internet? Intheory, feedback systems such as eBay’s should be able to help reduce fraud and provideincentives for sellers to be truthful. Yin (2005) suggests there is a relationship betweenfeedback and information: the better a seller’s reputation the greater the incentive for theseller to provide accurate information. Jin also finds that reputation matters. Sellers withhigh feedback scores do not make incredible claims and were much more likely to deliver thecard. Luis Cabral and Ali Hortacsu look at eBay’s feedback scores over time (Cabral andHortacsu (2006)). Cabral and Hortacsu find that a first negative report has a big effect onprice and that negatives beget negatives. Cabral and Hortacsu argue that sellers seem tochange their behavior and perform worse after receiving negatives. One explanation is thatsellers set up high feedback scores and then “milk” those scores by defrauding people andthen finally abandoning the eBay ID and creating a new eBay identity. 9 According to Jin,they observed this behavior on a number of occasions. Chris Dellarocas (Maryland’s Smith

8 Same city bias refers to the tendency for bidders to only bid on items sold by sellers in the same city, even ifthey could get the product at lower prices from sellers located in different cities (Hortacsu et al (2005)). Thismay occur because they have greater trust in sellers that are local or it may be because local buyers and sellershave similar preferences.9 Sellers and buyers on eBay are identified by their handles, for example “ebayuser64” or “bigseller88”. EBayusers can click on someone’s handle and find out more about them including their feedback rating, how longthey have been a member of the site, and comments on recent transactions. EBay tries to make it difficult forpeople to abandon handles and create new ones (without the associated bad feedback and comment history)but it is certainly possible for users to do this.

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School of Business) suggests that this explanation is not necessarily what is happening.Recent research finds that feedback in general slows down after the first negative and so thefact that a negative is a low probability event it may look “as if” negatives beget morenegatives (Khopkar et al. (2005)). What about law enforcement? Hampton Finer in the New York Attorney General’soffice, described work of the office to go after fraud and shilling on eBay. According toFiner, New York considers shilling an antitrust violation, but for the most part the NYAGlooks at cases where sellers are trying to defraud the customer by pretending to havelegitimate buyers bidding on a fraudulent item. The FTC’s Debbie Matties discussed theFTC’s role. The FTC does not pursue criminal cases but it does coordinate with criminalauthorities and it pursues civil remedies. The FTC also works with eBay to educate eBay’scustomers and help them to detect and avoid fraud on the site.

The Biggest Auction in the World

Hal Varian (University of California – Berkeley) gave the keynote address at theroundtable. Varian argues that Google’s Adword auctions are the largest auctions in theworld. 10 Google has about 30 billion keyword auctions per year and is growing at 26% peryear. Google began by pricing keywords “by hand”. However, this method becameunworkable and they needed to automatically price the keywords. At first, Google used a“first-price” auction. That is, bidders paid the amount that they bid. Note that the“product” is a click through: a bidder pays so much money per thousand click-throughs for aposition on the screen. Google found that the first price system led to considerable gaming.Bidders would reduce their bids and try to work out the lowest amount to bid and still keeptheir position. Google found that this behavior was wasting a substantial amount ofcomputer time so they introduced a “second-price” auction. In such an auction, the winningbidder pays the amount of the second highest bidder. Google’s auctions are quite different from an eBay auction or a standard Englishauction (Varian (2006)). Google sells multiple “positions” in a single auction. That is, the

10 “Adword” is the name that Google uses for its keyword search auctions. See Footnote 2.

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winner gets the best position on the page, the second highest bidder gets the second bestposition, the third highest bidder gets the third position, etc. Each bidder pays the priceequal to the winner of the position below them and the value of the position depends on theclick-through rates which are different for each position. (Note: Google provides bidderswith information on the expected click-through rate for each position.) The other differenceis that these auctions are continuous. Bidders can enter at any time, bidders can change theirbid at any time, and positions on the page change continuously with the bids. How do bidders behave in such auctions? Varian discussed his analysis of theseauctions and showed that the Nash equilibrium (i.e. each bidder chooses their optimal bid inresponse to the bids of all the other bidders) of such an auction will lead to a particularpattern of pricing. In particular, expenditures will be increasing with the click-through rateat an increasing rate. Looking at actual bids in actual keyword auctions, Varian finds that thetheoretical prediction is born out in the data.

Competition

The competition segment of the roundtable was the most wide-ranging in its coverage. Thesegment included a presentation by George Deltas (University of Illinois) on competitionbetween auction sites with the existence of network effects and a presentation by DavidReiley (University of Arizona) on competition between bidders for a particular item. Otherparticipants included Rana Kulpreet, Google’s in-house competition counsel, Lorenzo Coppifrom Charles River Associates, Hampton Finer from the NYAG’s office, Robert Marshallfrom Penn State and Bates White, and Lawrence Coffin an eBay seller and trading assistant. George Deltas presented recent work on pricing and competition between auctionsites when there exist network externalities (Deltas and Jeitschko (2006)). In the model thereis a feedback effect. Buyers want to be where the sellers are and sellers want to be where thebuyers are. Importantly, the model assumes that more sellers make the marginal seller morelikely to use the site. More sellers beget more sellers. This feedback has a number ofimplications. In particular, the owner of the site must be concerned about overcharging. Ifovercharging causes sellers to leave the market, the feedback effect will cause the market tocollapse. According to Deltas, the pricing power of the site owner may be much less than in

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a traditional market. However, predation may be a greater problem in such a market. A

small change in price could cause a competitor’s market to collapse and make it difficult fora competitor to restart or for another firm to enter the market. David Reiley looked at competition between bidders on a site (Gray and Reiley(2004)). Sniping, or last minute bidding, is very common in Internet auctions. Roth andOckenfels (2002) argue that a possible explanation for sniping is tacit collusion betweenbidders. 11 Others suggest that sniping is a good strategy for winning auctions at lowerprices. Reiley tests these explanations using a field experiment in which the researchers bidthe same amount on identical items sold by the same seller with early and late bids. Earlybids are placed three days before the end of the auction and late bids are placed withinseconds of the auction close. The authors find no statistically or economically significantdifference between prices paid in auctions won with differently timed bids. This resultsuggests that there is no advantage (in terms of price paid) to late bidding and seems to ruleout some explanations for sniping. The panelists discussed a number of issues in relation to competition in onlineauctions. Lawrence Coffin discussed and questioned recent moves by state governmentsand state licensing boards to require licensing and training programs for eBay tradingassistants. Lorenzo Coppi presented an analysis of the B2B online auction market. Coppishowed that the market collapsed after initial excitement and entry (see Exhibit 1). Coppiargues that network externalities provide the explanation for the failure. Large purchasershad no incentive to help establish a market and give their competitors access to low costsuppliers. Suppliers had little incentive to establish a market which increased competitionand lowered prices. Small purchasers did have an incentive to establish such a market butthey were not large enough to attract enough suppliers. Bob Marshall ended the discussionwith a presentation on the value of online auctions for government procurement and sales.In particular, Marshall argued that online auctions could reduce collusion by providing

11 Sniping refers to bids that come into the auction at the very last second before the auction closes. EBayauctions have a “hard close”, that is, these auctions end at an exact time no matter what is happening to theprice or the bidding. Other auction sites use a “going, going, gone” format which automatically continues theauction until there has been no bid for a particular amount of time (say 10 minutes) (Roth and Ockenfels(2002))

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competition authorities with a substantial amount of information about the behavior of

bidders. 12

Source: Day et al. (2002).

Inference From Bid Data

Vickrey (1961) suggests that bidders in an eBay-type auction will bid their value for the item.This result suggests that auctions sites like eBay could be very important in determining thevalue of items and thus useful in estimating productivity growth or the potential effects ofmergers. This last session included presentations by Axel Ockenfels of the University ofCologne and Robert Zeithammer of the University of Chicago GSB. Both presentationslooked at the question of whether bidders on eBay behave in a way that is consistent withVickrey’s model. Robin Sickles (Rice University) presented a method for estimating

12 For more on Marshall’s presentation seehttp://www.bateswhite.com/news/pdf/2005_Marshall_FTC_auctions.pdf.

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consumer surplus (consumer welfare) from eBay data. The roundtable ended with panelpresentations from Galit Shmueli of Maryland’s Smith School of Business, Jeff Hermann ofNielsen Media, Sean Peoples of Edmunds.com, and Ana Aizcorbe of the Bureau ofEconomic Analysis. Axel Ockenfels presented recent work from field experiments and laboratoryexperiments. In the field experiment they contacted potential participants and told them tobid in an actual eBay auction in which the researchers were the seller. The participants weretold how much money they would receive if they won the auction. By telling differentparticipants how much they would receive they could test whether eBay bidders actually bidtheir value for the item. They found bidders tended to bid their value. Ockenfels alsopresented preliminary results analyzing behavior in eBay’s multi-unit auctions. Robert Zeithammer looked at how eBay bidders bid when they face a sequence ofauctions for similar or identical items (Zeithammer (2005)). Recent theory suggests that ifbidders face a sequence of auctions, these bidders should shade their bids downwards inorder to account for the “option value” of losing the current auction and being able to bid ina future auction (Adams (2004)). While Zeithammer does not test this explicitly he doeslook at how behavior changes when bidders find out about a new auction. On eBay, biddersmay know that there is some possibility of there being a future auction for their preferredproduct, or they may know that there actually is a future auction for their preferred product.Zeithammer argues that if bidders know about the existence of a future auction for certain,their bids will decrease relative to knowing about the possibility of a future auction. Lookingat data on DVDs, Zeithammer finds that bids drop 3 – 7% when the bidder finds out thereis going to be a future auction for their preferred DVD title. Robin Sickles uses eBay data to estimate the consumer surplus generated by eBay(Giray et al. (2006)). While it is simple to estimate the revenue generated by eBay, it is notobvious how to estimate how much buyers value using the site. Sickles looks at bidding oncomputer monitors and estimates how much the winner of the auction values the item.Consumer surplus is then equal to the difference between the value and the price. Theauthors don’t observe the highest bid, so they make distributional assumptions to estimatethe item’s value and test how estimates change under different assumptions. The authors’preliminary work suggests that the consumer’s share of total consumer and producer surplusis between 30% and 61%. Ravi Bapna of the University of Connecticut reviewed the paper

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and discussed his own work to estimate consumer surplus for the whole of eBay. Bapnaestimates that eBay consumers generate at least 18% of total surplus or $6.5 billion (Bapna etal. (2005)). The roundtable ended with a number of presentations on the value of eBay data.Jeff Hermann of Nielsen Media presented results from Nielsen’s survey of Internetbehavior. The data show that eBay is one of the most visited sites on the Internet and theeBay Motors site is the most important in terms of expenditure. 13 One cautionary note inusing eBay data is the result that people who spend more on the site tend to be people usinghigh speed connections and high speed connections tend to be available to higher incomepeople. Another cautionary note on eBay data was raised by Galit Shmueli, who suggestedthat “spiders” that collect data off a site like eBay may not be providing the researcher with arandom sample of auctions. 14 The particular mechanisms that spiders use to search throughwebpages may lead to biases in the data. Sean Peoples of Edmunds.com presented researchanalyzing the relative depreciation of used cars sold on eBay. Peoples found that cars with alot more options tend to depreciate faster and suggested that buyers may have difficultydetermining which options are actually available on a particular model. Peoples also notedthat eBay prices tend to track prices in the wholesale auction market, suggesting that thereason people like eBay motors is that they get a good deal. Ana Aizcorbe ended the sessionwith a suggestion that data from Internet auctions may be useful for estimating price indexesand quality change (Aizcorbe (2002)). Aizcorbe described a problem with identifying qualitychange using traditional transactions data. The standard assumption is that there is arepresentative consumer that purchases the same product in every period. In this model aprice reduction leads to a large increase in consumer welfare. However, if later purchasers ofa product have a lower value for the item, the estimated change in consumer welfare is muchlower.

13 EBay Motors is a separate site that sells motorized items, particularly cars (Adams et al. (2006)).14 A “spider” refers to a computer program that automatically searchers websites and downloads webpages.

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Conclusion

In organizing this roundtable I hoped to bring together academic researchers and industryprofessionals to start a dialog that would lead to improvements in our understanding ofonline auction design and how these auctions work in practice. EBay, Google and othersuccessful Internet businesses rely on online auctions and attribute their success to the valueof these market mechanisms. This roundtable with its associated papers, presentations anddiscussions greatly increases our understanding of these important market mechanisms.

Anderson, Keith, 2005, “Internet Auction Fraud: What Can We Learn from ConsumerSentinel Data?” Presentation for the FTC Roundtable on the Economics of InternetAuctions, October 2005(http://www.ftc.gov/be/workshops/internetauction/keithandersonslides.pdf).

Deltas, George and Thomas Jeitschko, 2006, “Auction Hosting Site Pricing andCompetition,” Working Paper, University of Illinois, February athttps://netfiles.uiuc.edu/deltas/www/UnpublishedWorkingPapers/AuctionSitePricing.pdf